Format
Sort by
Items per page

Send to

Choose Destination

Links from PubMed

Items: 1 to 20 of 429

1.

Computational procedures for optimal experimental design in biological systems.

Balsa-Canto E, Alonso AA, Banga JR.

IET Syst Biol. 2008 Jul;2(4):163-72. doi: 10.1049/iet-syb:20070069.

PMID:
18681746
2.

Efficient classification of complete parameter regions based on semidefinite programming.

Kuepfer L, Sauer U, Parrilo PA.

BMC Bioinformatics. 2007 Jan 15;8:12.

3.

Optimal experimental design with the sigma point method.

Schenkendorf R, Kremling A, Mangold M.

IET Syst Biol. 2009 Jan;3(1):10-23. doi: 10.1049/iet-syb:20080094.

PMID:
19154081
4.

Optimal sampling time selection for parameter estimation in dynamic pathway modeling.

Kutalik Z, Cho KH, Wolkenhauer O.

Biosystems. 2004 Jul;75(1-3):43-55.

PMID:
15245803
5.

Decoupling dynamical systems for pathway identification from metabolic profiles.

Voit EO, Almeida J.

Bioinformatics. 2004 Jul 22;20(11):1670-81. Epub 2004 Feb 26.

PMID:
14988125
6.

The Beta Workbench: a computational tool to study the dynamics of biological systems.

Dematté L, Priami C, Romanel A.

Brief Bioinform. 2008 Sep;9(5):437-49. doi: 10.1093/bib/bbn023. Epub 2008 May 7.

PMID:
18463130
7.

A hybrid approach for efficient and robust parameter estimation in biochemical pathways.

Rodriguez-Fernandez M, Mendes P, Banga JR.

Biosystems. 2006 Feb-Mar;83(2-3):248-65. Epub 2005 Oct 19.

PMID:
16236429
8.

Dynamic pathway modeling: feasibility analysis and optimal experimental design.

Maiwald T, Kreutz C, Pfeifer AC, Bohl S, Klingmüller U, Timmer J.

Ann N Y Acad Sci. 2007 Dec;1115:212-20.

PMID:
18033750
9.
10.

Addressing parameter identifiability by model-based experimentation.

Raue A, Kreutz C, Maiwald T, Klingmuller U, Timmer J.

IET Syst Biol. 2011 Mar;5(2):120-30. doi: 10.1049/iet-syb.2010.0061.

PMID:
21405200
11.

S-system parameter estimation for noisy metabolic profiles using newton-flow analysis.

Kutalik Z, Tucker W, Moulton V.

IET Syst Biol. 2007 May;1(3):174-80.

PMID:
17591176
12.

Gene regulatory network inference: data integration in dynamic models-a review.

Hecker M, Lambeck S, Toepfer S, van Someren E, Guthke R.

Biosystems. 2009 Apr;96(1):86-103. doi: 10.1016/j.biosystems.2008.12.004. Epub 2008 Dec 27. Review.

PMID:
19150482
13.

Multi-objective mixed integer strategy for the optimisation of biological networks.

Sendín JO, Exler O, Banga JR.

IET Syst Biol. 2010 May;4(3):236-48. doi: 10.1049/iet-syb.2009.0045.

PMID:
20500003
14.

A structured approach for the engineering of biochemical network models, illustrated for signalling pathways.

Breitling R, Gilbert D, Heiner M, Orton R.

Brief Bioinform. 2008 Sep;9(5):404-21. doi: 10.1093/bib/bbn026. Epub 2008 Jun 23. Review.

PMID:
18573813
15.
16.

Improved parameter estimation for systems with an experimentally located Hopf bifurcation.

Cedersund G, Knudsen C.

Syst Biol (Stevenage). 2005 Sep;152(3):161-8.

PMID:
16986279
17.

Data requirements of reverse-engineering algorithms.

Just W.

Ann N Y Acad Sci. 2007 Dec;1115:142-53. Epub 2007 Oct 9.

PMID:
17925350
18.

Parameter identification, experimental design and model falsification for biological network models using semidefinite programming.

Hasenauer J, Waldherr S, Wagner K, Allgöwer F.

IET Syst Biol. 2010 Mar;4(2):119-30. doi: 10.1049/iet-syb.2009.0030.

PMID:
20232992
19.

Dynamical modeling and multi-experiment fitting with PottersWheel.

Maiwald T, Timmer J.

Bioinformatics. 2008 Sep 15;24(18):2037-43. doi: 10.1093/bioinformatics/btn350. Epub 2008 Jul 9.

20.

Parameter estimation and optimal experimental design.

Banga JR, Balsa-Canto E.

Essays Biochem. 2008;45:195-209. doi: 10.1042/BSE0450195. Review.

PMID:
18793133

Supplemental Content

Support Center